https://github.com/ngupta23/ml-stanford-coursera

Machine Learning Course (Andrew Ng, Stanford) from Coursera
https://github.com/ngupta23/ml-stanford-coursera

machine-learning machine-learning-algorithms machine-learning-coursera

Last synced: 13 days ago
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Machine Learning Course (Andrew Ng, Stanford) from Coursera

• Host: GitHub
• URL: https://github.com/ngupta23/ml-stanford-coursera
• Owner: ngupta23
• Created: 2018-02-09T12:03:26.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-02-28T10:52:08.000Z (almost 7 years ago)
• Last Synced: 2024-10-28T16:53:19.094Z (2 months ago)
• Topics: machine-learning, machine-learning-algorithms, machine-learning-coursera
• Language: Matlab
• Size: 23.6 MB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ML-Stanford-Coursera

Machine Learning Course (Andrew Ng, Stanford) from Coursera 

https://www.coursera.org/learn/machine-learning/home/welcome

This reporsitoy includes all the programming exercises from the course.

The programs were written in Octave

**Exercises include following algorithms**

1. Linear Regression

2. Logistic Regression

3. Neural Networks

4. Support Vector Machines

5. K-means Clustering

6. Anomaly Detection

7. Recommender Systems

**Concepts applied**

1. Vectorize the code whenever possible - especially in ex7 (image compression and clustering). The difference in execution between non vectorized and vectorized was immense. Totally worth the time to implement the vectorized code! 

2. Feature Scaling - When to use

3. Regularization - Application, Finding the optimim value

4. Cost Function and Gradient calculations

5. Use of advanced learning algorithms

6. Debugging the model using learning curves - Diagnosing Bias vs. Variance

7. Train/Cross Validation/Test set application

8. Dimensionality Reduction - PCA (Principal Component Analysis)

9. Kernels as applied to SVM